This paper presents a system for vehicle detection, tracking and classification from roadside CCTV. The system counts vehicles and separates them into four categories: car, van, bus and motorcycle (including bicycles). A new background Gaussian Mixture Model (GMM) and shadow removal method have been used to deal with sudden illumination changes and camera vibration. A Kalman filter tracks a vehicle to enable classification by majority voting over several consecutive frames, and a level set method has been used to refine the foreground blob. Extensive experiments with real world data have been undertaken to evaluate system performance. The best performance results from training a SVM (Support Vector Machine) using a combination of a vehicle silhouette and intensity-based pyramid HOG features extracted following background subtraction, classifying foreground blobs with majority voting. The evaluation results from the videos are encouraging: for a detection rate of 96.39%, the false positive rate is only 1.36% and false negative rate 4.97%. Even including challenging weather conditions, classification accuracy is 94.69%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle detection, tracking and classification in urban traffic


    Contributors:


    Publication date :

    2012-09-01


    Size :

    403500 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle Detection and Tracking in Vietnam’s Complex Urban Traffic

    Nguyen, Anh Lan / Pham, Tung Xuan | Springer Verlag | 2025


    Stereovision Based Vehicle Tracking in Urban Traffic Environments

    Danescu, R. / Nedevschi, S. / Meinecke, M.M. et al. | IEEE | 2007


    Traffic Vehicle Tracking and Trajectory Classification Using LSTM

    Konapalli, Kumar / Peddapothula, YaswanthPavan / Ghosh, Nirmalya | IEEE | 2024


    Developing a Framework for Vehicle Detection, Tracking and Classification in Traffic Video Surveillance

    Saha, Rumi / Debi, Tanusree / Arefin, Mohammad Shamsul | Springer Verlag | 2021